1. Field
At least one aspect relates to signal processing and, more particularly, processing techniques used in conjunction with blind source separation (BSS) techniques.
2. Background
Some mobile communication devices may employ multiple microphones in an effort to improve the quality of the captured sound and/or audio signals from one or more signal sources. These audio signals are often corrupted with background noise, disturbance, interference, crosstalk and other unwanted signals. Consequently, in order to enhance a desired audio signal, such communication devices typically use advanced signal processing methods to process the audio signals captured by the multiple microphones. This process is often referred to as signal enhancement which provides improved sound/voice quality, reduced background noise, etc., in the desired audio signal while suppressing other irrelevant signals. In speech communications, the desired signal usually is a speech signal and the signal enhancement is referred to as speech enhancement.
Blind source separation (BSS) can be used for signal enhancement. Blind source separation is a technology used to restore independent source signals using multiple independent signal mixtures of the source signals. Each sensor is placed at a different location, and each sensor records a signal, which is a mixture of the source signals. BSS algorithms may be used to separate signals by exploiting the signal differences, which manifest the spatial diversity of the common information that was recorded by both sensors. In speech communication processing, the different sensors may comprise microphones that are placed at different locations relative to the source of the speech that is being recorded.
Beamforming is an alternative technology for signal enhancement. A beamformer performs spatial filtering to separate signals that originate from different spatial locations. Signals from certain directions are amplified while the signals from other directions are attenuated. Thus, beamforming uses directionality of the input signals to enhance the desired signals.
Both blind source separation and beamforming use multiple sensors placed at different locations. Each sensor records or captures a different mixture of the source signals. These mixtures contain the spatial relationship between the source signals and sensors (e.g., microphones). This information is exploited to achieve signal enhancement.
In communication devices having closely spaced microphones, the captured input signals from the microphones may be highly correlated due to the close proximity between the microphones. In this case, traditional noise suppression methods, including blind source separation, may not perform well in separating the desired signals from noise. For example, in a dual microphone system, a BSS algorithm may take the mixed input signals and produce two outputs containing estimates of a desired speech signal and ambient noise. However, it may not be possible to determine which of the two output signal is the desired speech signal and which is the ambient noise after signal separation. This inherent indeterminacy of BSS algorithms causes major performance degradation.
Consequently, a way is needed to improve the performance of blind source separation on communication devices having closely spaced microphones.